Biology subjects

Basu, S.

Publications and source records attributed to Basu, S..

11 recordsLinked to original sources

bioRxiv2026

SOX2 can associate with chromatin directly by binding to DNA or indirectly via association with other chromatin-bound proteins

It is widely assumed that SOX2 regulates gene expression and facilitates the opening of chromatin by binding directly at SOX motifs. To test this assumption, we created a SOX2 DNA binding mutant to determine whether other regions of SOX2 contribute to gene target specificity. When exogenously expressed in cells, this SOX2 mutant [SOX2(G76P)], like elevated unmodified SOX2, dramatically alters the transcriptome, but it does so by regulating vastly different gene sets and gene networks than SOX2. Consistent with their differential effects on the transcriptome, ChIP-seq analysis demonstrates that SOX2 and SOX2(G76P) associate primarily with different genomic loci, and motif analysis indicates that SOX2 binds primarily at SOX motifs, whereas SOX2(G76P) associates with chromatin at non-SOX motifs, including AP-1 motifs. Additionally, ATAC-seq analysis indicates that SOX2 substantially increases chromatin accessibility, but SOX2(G76P) does not. The findings presented lead to the conclusion that SOX2(G76P) associates with chromatin indirectly by a "piggyback" mechanism through its association with other chromatin-associated proteins, including AP-1 complexes. Remarkably, we also show that SOX2 and SOX2(G76P) each associate with a subset of the same gene loci that contain several different DNA motifs, including AP-1 motifs, but no high confidence SOX motifs. Overall, our findings provide new perspectives on SOX2 and lead to two important conclusions: 1) selection of gene targets by SOX2 is not solely determined by its DNA binding domain, and 2) SOX2 not only associates with chromatin directly by binding to SOX motifs but can also associate with a subset of gene loci indirectly through its association with other chromatin-associated proteins.

molecular biology↗

bioRxiv2018

A multifaceted approach for analyzing complex phenotypic data in rodent models of autism

AutDB features a modular framework that aims at collating multifactorial risk factors associated with autism spectrum disorder (ASD). The animal model (AM) module of AutDB was first developed for mouse models of genes and CNVs associated with ASD (Kumar et al., 2011). Subsequently, environmentally induced rodent models were introduced to capture the full spectrum of risk-factors associated with ASD, along with idiopathic models represented by inbred strains. Using the data systematically annotated in AutDB, we depict the intricate trends in the research findings based on rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from 911 genetic, 269 induced and 17 inbred rodent models of ASD extracted from 787 publications. As expected, many of these include animal model equivalents of the core phenotypes associated with ASD, as well as several comorbid features of ASD including anxiety, seizures and motor-control deficits. Uniquely, AutDB curates rescue models where various treatment strategies were used in rodent ASD models to alleviate ASDrelevant phenotypes. We further examine ASD models based on 52 genes and 2 CNV loci to identify 24 pharmaceutical agents that were used in 2 or more paradigms for testing their efficacy. As a case study, we analyze various Shank3 mouse models providing a highresolution view of the in vivo role of this high-confidence ASD gene. Together, this resource provides a snapshot of genetic and induced models of ASD within a shared annotation platform to examine the complex meshing of diverse ASD-associated risk-factors.

bioinformatics↗

bioRxiv2018

Structure of a human intramembrane ceramidase explains enzymatic dysfunction found in leukodystrophy

Alkaline ceramidases (ACERs) are a class of poorly understood transmembrane enzymes controlling the homeostasis of ceramides. They are implicated in human pathophysiology, including progressive leukodystrophy, colon cancer as well as acute myeloid leukemia. We report here the crystal structure of the human ACER type 3 (ACER3). Together with computational studies, the structure reveals that ACER3 is an intramembrane enzyme with a seven transmembrane domain architecture and a catalytic Zn2+ binding site in its core, similar to adiponectin receptors. Interestingly, we uncover a Ca2+ binding site physically and functionally connected to the Zn2+ providing a structural explanation for the known regulatory role of Ca2+ on ACER3 enzymatic activity and for the loss of function in E33G-ACER3 mutant found in leukodystrophic patients.

biochemistry↗

bioRxiv2018

Insights into the evolution of extracellular leucine rich repeats in metazoans with special reference to Toll-like receptor 4

The importance of the widely spread leucine-rich repeat (LRR) motif has been studied considering TLRs, the LRR-containing protein involved in animal immune response. The protein connects intracellular signalling with a chain of molecular interaction through the presence of LRRs in the ectodomain and TIR in the endodomain. Domain analyses with human TLR1-9 reported ectodomain with tandem repeats, transmembrane domain and TIR domain. The repeat number varied across members of TLRs and remains characteristic to a particular member. Analysis of gene structure revealed absence of codon interruption with TLR3 and TLR4 as exceptions. Extensive study with TLR4 from metazoans confirmed the presence of 23 LRRs in tandem. Distinct clade formation using coding and amino acid sequence of individual repeats illustrated independent evolution. Although ectodomain and endodomain exhibited differential selection pressure, however, within the ectodomain, the individual repeats displayed positive, negative and neutral selection pressure depending on their structural and functional significance.

bioinformatics↗

bioRxiv2017

iterative Random Forests to discover predictive and stable high order interactions

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.

genomics↗

bioRxiv2017

Salt-bridge Dynamics in Intrinsically Disordered Proteins: A trade-off between electrostatic interactions and structural flexibility

Intrinsically Disordered Proteins (IDPs) are enriched in charged and polar residues; and, therefore, electrostatic interactions play a predominant role in their dynamics. In order to remain multi-functional and exhibit their characteristic binding promiscuity, they need to retain considerable dynamic flexibility. At the same time, they also need to accommodate a large number of oppositely charged residues, which eventually lead to the formation of salt-bridges, imparting local rigidity. The formation of salt-bridges therefore oppose the desired dynamic flexibility. Hence, there appears to be a meticulous trade-off between the two mechanisms which the current study attempts to unravel. With this objective, we identify and analyze salt-bridges, both as isolated as well as composite ionic bond motifs, in the molecular dynamic trajectories of a set of appropriately chosen IDPs. Time evolved structural properties of these salt-bridges like persistence, associated secondary structural 'order-disorder' transitions, correlated atomic movements, contribution in the overall electrostatic balance of the proteins have been studied in necessary detail. The results suggest that the key to maintain such a trade-off over time is the continuous formation and dissolution of salt-bridges with a wide range of persistence. Also, the continuous dynamic interchange of charged-atom-pairs (coming from a variety of oppositely charged side-chains) in the transient ionic bonds supports a model of dynamic flexibility concomitant with the well characterized stochastic conformational switching in these proteins. The results and conclusions should facilitate the future design of salt-bridges as a mean to further explore the disordered-globular interface in proteins.

bioinformatics↗

bioRxiv2017

Endogenous α-SYN protein analysis on human brain tissues using single-molecule pull-down assay

Alpha-synuclein (-SYN) is a central molecule in Parkinsons disease pathogenesis. Despite several studies, the molecular nature of endogenous -SYN especially in human brain samples is still not well understood due to the lack of reliable methods and the limited amount of bio-specimens. Here, we introduce -SYN single-molecule pull-down (-SYN SiMPull) assay combined with in vivo protein crosslinking to count individual -SYN protein and assess its native oligomerization states from biological samples including human postmortem brains. A SiMPull assay enables us to count the number of immuno-precipitated proteins, and reveal the stoichiometry of protein complexes by single-molecule fluorescence imaging. This powerful assay can be highly useful in diagnostic applications using various specimens for neurodegenerative diseases including Alzheimers disease and Parkinsons disease.

biophysics↗

bioRxiv2017

CPdock: The Complementarity Plot for Docking of Proteins: Implementing Multi-dielectric Continuum Electrostatics

The Complementarity plot (CP) is an established validation tool for protein structures, applicable to both, globular proteins (folding) as well as protein-protein complexes (binding). It computes the shape and electrostatic complementarities (Sm, Em) for amino acid side-chains buried within the protein interior or interface and plots them in a two-dimensional plot having knowledge-based probabilistic quality estimates for the residues as well as for the whole structure. The current report essentially presents an upgraded version of the plot with the implementation of the advanced multi-dielectric functionality (as in Delphi version 6.2 or higher) in the computation of electrostatic complementarity to make the validation tool physico-chemically more realistic. The two methods (single- and multi-dielectric) agrees decently in their resultant Em values and hence, provisions for both methods have been kept in the software suite. So to speak, the global electrostatic balance within a well-folded protein and / or a well-packed interface seems only marginally perturbed by the choice of different internal dielectric values. However, both from theoretical as well as practical grounds, the more advanced multi-dielectric version of the plot is certainly recommended for potentially producing more reliable results. The report also presents a new methodology and a variant plot, namely, CPdock, based on the same principles of complementarity, specifically designed to be used in the docking of proteins. The efficacy of the method to discriminate between good and bad docked protein complexes have been tested on a recent state-of-the-art docking benchmark. The results unambiguously indicate that CPdock can indeed be effective in the initial screening phase of a docking scoring pipeline before going into more sophisticated and computationally expensive scoring functions. CPdock has been made available at https://github.com/nemo8130/CPdock

bioinformatics↗

bioRxiv2017

A Zebrafish Model For Ocular Tuberculosis

Ocular tuberculosis (TB) commonly causes severe inflammation and vision loss in TB-endemic countries. The mechanism by which tuberculous infection becomes established in the eye is poorly understood. We used Mycobacterium marinum-infected zebrafish larvae to study the early pathogenesis of ocular TB and found hematogenous bacterial seeding of the eye despite a functional blood retinal barrier. Prototypical early granulomas formed that involved the retinal vasculature and retinal pigment epithelium-choroid complex; characteristic locations for human ocular TB. Peripheral blood monocytes were recruited to the growing granuloma suggesting that the immune privileged nature of the eye is breached by this inflammatory focus.\n\nConflict of interestnone disclosed\n\nFundingThis work was supported in part by a Short-term fellowship to SB by Department of Health Research, Government of India.

microbiology↗

bioRxiv2017

Nitric oxide sensing by chlorophyll a

Nitric oxide (NO) acts as a signalling molecule that has direct and indirect regulatory roles in various functional processes in biology, though in plant kingdom its role is relatively unexplored. One reason for this is the fact that sensing of NO is always challenging. There are very few probes that can classify the different NO species. The present paper proposes a simple but straightforward way for sensing different NO species using chlorophyll, the source of inspiration being hemoglobin that serves as a NO sink in most mamalian system. The proposed method is able to classify NO from DETA-NONOate or (Z)-1-[N-(2-aminoethyl)-N-(2-ammonioethyl) amino] diazen-1-ium-1,2-diolate, nitrite, nitrate and S-nitrosothiol or SNO. This discrimination is carried out by chlorophyll a (chl a) at nano molar (nM) order of sensitivity and at 293K to 310K. Molecular docking reveals the differential binding behaviour of NO and SNO with chlorophyll, the predicted binding affinity matching with the experimental observation. Additional expreiments with diverse range of cyanobacteria reveals that apart from spectroscopic approach the proposed sensing module can be used in microscopic inspection of NO speices. Binding of NO is sensitive to tempertaure and static magnetic field. This provides additional support to the involvement of the porphyrin ring structure to the NO sensing process. This also broadens the scope of the sensing methods as hinted in the text.

bioengineering↗

bioRxiv2016

Proteus: A Random Forest Classifier to Predict disorder-to-order Transitioning Binding Regions in Intrinsically Disordered Proteins.

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs / IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP / IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55% vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.\n\nSoftware Availabilityhttps://github.com/bjornwallner/proteus

bioinformatics↗